As imaging technology has evolved, the density of imaging data has increased. Larger amounts of imaging data reduces speed and performance of imaging devices. Accordingly, a variety of approaches have been developed to improve the speed of imaging systems.
FIG. 1 shows a block diagram of a conventional integrated sensor imager device fabricated on a chip. An imaging sensor 1 with an array of pixels transmits analog pixel signals to a sample and hold analog signal processing circuit 2. The analog output signals from circuit 2 are digitized in an analog to digital converter 3 and the digital signals are processed in an image processor 4. Imaging data is read out of the processor 4 and sent to a serializer 5. The serializer 5 sends imaging data to a transmission unit 7. In some sensors systems, such as the one illustrated in FIG. 1, acquired images show a great deal of correlation from one pixel value to the next. Images with correlation attributes typically exhibit a low entropy when individual differences from one sample to the next are considered for compression (and transmission) instead of the samples from the original image. Entropy is defined as the amount of information content uncertainty of some random variable X. Images that exhibit a high degree of random values are said to have a high entropy, while images where most pixels have the same value are said to exhibit a low entropy.
The performance of imaging systems can be improved by using compression techniques. Imaging data compression can be grouped into two categories: lossy and lossless compression. Lossless and lossy compression are terms that describe whether or not, in the compression of a file, all original data can be recovered when the file is uncompressed. With lossless compression, every single bit of data that was originally in the file remains after the file is uncompressed. That is, all of the information is completely restored during the decompression of the data. Lossless compression is generally the technique of choice for text or spreadsheet files, where losing words or financial data could pose a problem. The Graphics Interchange File (GIF) is an image format that provides lossless compression.
Lossy compression, on the other hand, reduces a file by permanently eliminating certain information, especially redundant information. When the file is uncompressed, only a part of the original information is still there (although the user may not notice it). Lossy compression is generally used for video and sound, where a certain amount of information loss will not be detected by most users. The JPEG image file, commonly used for photographs and other complex still images, uses lossy compression techniques. JPEG compression allows a creator to decide how much loss to introduce and make a trade-off between file size and image quality.
Real-time lossless compression of images acquired from an image sensor can be accomplished using custom logic integrated on the same chip as the imager sensor. It is possible to accomplish lossless compression with a minimal amount of dedicated hardware.
As density of image data to be transmitted or communicated increases, designers have tried to find ways to maximize lossless compression of the image while at the same time minimizing the amount of dedicated hardware required. Also, many applications require the use of lossless compression due to additional design constraints. For example, remote endoscopy units in medical imaging applications have both hardware area and available bandwidth constraints. Variations of color and texture in medical imaging applications are very small and it is important, from a diagnostic point of view, that subtle variations are captured and not lost. Also, regulatory issues (e.g., FDA approval) will almost certainly be more complex in the with the use of lossy data compression, therefore data loss is undesirable. Accordingly, size, area and other constraints adversely affect speed and efficiency of conventional imaging systems. An increase in available bandwidth and performance due to an improved lossless compression is needed.